血浆β淀粉样蛋白在阿尔茨海默病早期诊断中的临床价值

目的 探究血浆Aβ42、Aβ40和Aβ42/Aβ40比值作为生物标志物在阿尔茨海默病和轻度认知障碍早期诊断中的价值.方法 入选受试者113例,其中44例阿尔茨海默病患者,33例轻度认知障碍患者和36例正常老年人,取静脉血测定其血浆Aβ42和Aβ40蛋白水平.结果 NC组、MCI组和AD组Aβ42水平和Aβ42/Aβ40比值逐渐下降.Aβ40水平逐渐升高,但差异无统计学意义.Aβ42水平可以区分NC组和AD组,但无法区分MCI组.Aβ40水平在各组之间比较差异无统计学意义.Aβ42/Aβ40比值在各组之间差异均具有统计学意义.结论 血浆Aβ42和Aβ40不能独立作为阿尔茨海默病早期诊断标志物,而它们的比值或许可以作为潜在的MCI和AD的诊断生物标志物,作为脑脊液检查和影像学检查的补充作用.     

壳聚糖磷脂酰胆碱抑制轻度认知障碍患者血清白细胞介素1β及肿瘤坏死因子α的表达**

背景：壳聚糖磷脂酰胆碱可通过血-脑脊液屏障改善阿尔茨海默病症状。 目的：观察壳聚糖磷脂酰胆碱对轻度认知障碍患者血清白细胞介素1β、肿瘤坏死因子α及简易精神状态量表MMSE评分的影响。 方法：从潍坊市部分社区及老人院按照轻度认知障碍诊断标准筛选36例轻度认知障碍患者,随机分为实验组(口服壳聚糖磷脂酰胆碱治疗)与空白对照组(不服药),并选择年龄、学历相匹配的健康对照。应用放射免疫分析法检测3组血清中白细胞介素1β和肿瘤坏死因子α水平。 结果与结论：实验组治疗后血清白细胞介素1β、肿瘤坏死因子α水平较治疗前明显降低,并明显低于空白对照组(P < 0.05);MMSE评分较治疗前明显提高(P < 0.05)。说明壳聚糖磷脂酰胆碱能改善轻度认知障碍患者认知功能,降低血清白细胞介素1β及肿瘤坏死因子α水平,减轻炎症反应,延缓病情进展。 关键词：壳聚糖磷脂酰胆碱;轻度认知障碍;白细胞介素1β;肿瘤坏死因子α;生物材料 doi:10.3969/j.issn.1673-8225.2012.08.040     

轻度认知功能障碍患者的语义记忆损害与神经调控

文题释义：轻度认知障碍：是介于正常衰老与痴呆的过渡状态,是相对于年龄与教育程度的记忆或其他认知功能减退,记忆力、语言功能、注意力、执行能力等不同认知领域的减退,以记忆力减退为最常见的临床表现。国际轻度认知功能障碍工作组在2003年制定了轻度认知障碍的诊断标准：①有认知主诉,本人或知情者提供的认知功能障碍线索;②有认知功能损害的客观证据,选用蒙特利尔认知评估量表进行评分,高中及以上文化程度者≤26分,初中及以下文化程度者≤25分,且主要表现为记忆项异常者;③日常生活能力正常或仅有复杂日常能力轻度减退;④简易认知状态量表≥24分,不符合精神疾病的诊断和统计手册第四版诊断为痴呆的标准。语义记忆：包括对词语的意义、概念与事实的记忆。语义记忆涉及概念和实际知识是储存。广义上语义记忆包括对世界的所有认识。严格意义上的语义记忆,则是根据命名、分类任务判定。语义记忆障碍的患者表现为对熟悉的物品命名障碍。轻度的可能表现为语义分类词生成的减少,而严重的语义记忆障碍则表现为告知物品用途也不能进行命名,或给予名称也不能说出物品的用途,严重者可能表现为常识的缺失。 背景：阿尔茨海默病是一种进行性神经系统退化疾病,以认知下降为主要特点。认知力下降会导致老年人逐渐失去自我照料的能力,影响日常生活和活动。轻度认知障碍作为阿尔茨海默病的前驱状态,尽早对轻度认知障碍进行诊断和治疗,对预防阿尔茨海默病发展有重大的意义。 目的：针对轻度认知障碍语义记忆障碍评估与治疗的发展及未来前景做一综述。 方法：应用计算机在PubMed、Web of Science和中国知网、万方等数据库检索轻度认知障碍语义记忆评估、治疗的相关研究,检索关键词为“mild cognitive impairment,semantic memory impairment,semantic memory deficit,语义记忆,轻度认知功能障碍,轻度认知损害”,检索时间为2009年1月至2019年11月。结果与结论：语义记忆障碍为轻度认知障碍的主要临床症状之一,有一定的特异性。目前有不同类型的记忆量表可作为轻度认知障碍语义记忆障碍的神经心理学测量,颞叶、额叶和前运动区可能参与语义记忆环路。针对性的语义记忆神经生理学及辅助检查,靶向进行语义记忆康复训练,可利于早期识别轻度认知障碍的发生与转化。 ORCID: 0000-0002-2121-1523(关汉添) 中国组织工程研究杂志出版内容重点：组织构建;骨细胞;软骨细胞;细胞培养;成纤维细胞;血管内皮细胞;骨质疏松;组织工程     

MRI有助预测阿尔茨海默病

据2011年4月6日McEvov LK(Radiology,2011 Apr 6.[Epub ahead of print])报道,利用MRI技术对脑部进行扫描,可以帮助医生预测轻度认知障碍患者今后是否会患阿尔茨海默病。     

轻度认知障碍患者EEG动力学特征提取与分类方法研究进展

脑电图可用于对轻度认知障碍的病理性变化进行评估。近年来,脑电领域的特征提取和分类方法广泛地应用到对轻度认知障碍疾病的诊断中。首先从局部耦合与全局同步两个方面,深入分析轻度认知障碍患者脑电信号特征提取方法的应用情况及其优势和不足,而后对当前轻度认知障碍患者脑电信号特征进行分类的多种方法进行详细总结与分析,如支持向量机、k均值以及近年来应用广泛的卷积神经网络等,最后对该领域脑电动力学特征提取与分类方法的未来发展趋势进行展望。     

血管性认知障碍发病机制的研究进展

血管性认知障碍(vascular cognitive impairment, VCI)是指由血管性因素引起的以认知障碍为特征的一组疾病,包括从轻度VCI到血管性痴呆.VCI被认为是继阿尔茨海默病(Alzheimer disease,AD)之后的第二大最常见的痴呆症类型疾病,患病人数约占痴呆患者总数的15％～20％[1]...     

阿尔茨海默病患者默认网络社团结构的分析

研究表明,默认网络(DMN)的功能失调与阿尔茨海默病有关。为进一步发现阿尔茨海默病患者大脑默认网络存在的异常连接结构,使用最小生成树方法构建无偏的脑网络,采用树层次聚类方法分析早期轻度认知障碍组(EMCI)、晚期轻度认知障碍组(LMCI)、阿尔茨海默病患者(AD)和健康对照者(NC) DMN社团结构的变化,并且对4种被试大脑网络中回直肌-眶部额上回、楔前叶-后扣带回的连接以及颞上回中心性进行差异分析。结果显示:DMN在NC和EMCI中分成5个社团,在LMCI中分成7个社团,但是在AD分成9个社团; LMCI和AD在回直肌-眶部额上回的连接存在显著差异(P=0. 048),LMCI和EMCI在楔前叶-后扣带回的连接存在显著差异(P=0. 042),LMCI和NC在楔前叶-后扣带回的连接存在显著差异(P=0. 016);颞上回介数中心性在AD组与LMCI组(P=0. 028)、LMCI组与NC组(P=0. 001)、EMCI组与NC组(P=0. 048)都存在显著差异。阿尔茨海默病患者随着病情的进展,DMN的结构逐渐分散,脑区之间的连接以及中心性发生变化,这些脑区主要包括海马、海马旁回、楔...     

基于极限学习机的阿尔兹海默病辅助诊断

阿尔兹海默病是一种渐进发展式的痴呆疾病, 其脑部随着病情发展逐渐出现萎缩。利用磁共振脑图像解剖学特征的变化, 提出一种使用极限学习机来诊断阿尔兹海默病以及轻度认知障碍的方法。采用FreeSurfer软件, 分析从ADNI数据库的818份磁共振图像中得到的脑部解剖学特征。首先对这些特征使用线性回归模型来估计正常衰老引起的萎缩因素, 并将其从特征中去除;随后采用极限学习机作为分类器, 使用处理后的特征来诊断阿尔兹海默病和轻度认知障碍。在实验过程中, 通过十折交叉验证来测试该方法的诊断准确率、敏感度、特异度和曲线下面积。通过100次实验求平均的方式计算得出, 该方法诊断阿尔兹海默病的准确率达到87.62%, 曲线下面积达到94.25%;诊断轻度认知障碍的准确率达到73.38%, 敏感度达到83.88%, 其中年龄矫正能有效提高轻度认知障碍诊断的准确率。实验结果表明, 该方法能有效诊断阿尔兹海默病和轻度认知障碍。     

氧化应激与老年痴呆症的发生发展关系

氧化应激被认为是正常老化和老年性疾病,如阿尔茨海默病(AD)、帕金森病(Parkinson‘ s disease, PD)等发病过程中的一个关键因素,这种应激在衰老过程中,随着脑组织氧化损伤而影响学习和记忆能力,在轻度认知障碍状态中发挥重要作用.本文拟在这方面做一些综述.     

基于中心化自动加权多任务学习的早期轻度认知障碍诊断

轻度认知障碍(MCI)是阿尔茨海默病(AD)的早期阶段,是治疗AD的最佳时期,因此对MCI的诊断非常重要。多模态数据可以全面分析疾病的状况,有利于疾病的准确诊断,但是现有方法并不能同时有效地分析多个模态数据之间的关系,无法有效结合功能态数据和结构态数据之间的优势。提出一种中心化自动加权多任务学习方法用于MCI的诊断。该方法可以同时学习不同模态的数据,有效地结合数据之间的优势。首先,分别对功能态数据rs-fMRI和结构态数据DTI构造脑网络;其次,基于多模态数据设计新的多任务特征学习模型,每个任务的重要性和模态之间的平衡关系会被自动学习,包括不同模态间的相似性和特异性,以获得稳定且有识别力的表达特征;最后,将选取的特征输入支持向量机模型进行分类诊断。实验基于Alzheimer′s Disease Neuroimaging Initiative(ADNI)公共数据库,包括明显记忆问题(SMC)、早期轻度认知障碍(EMCI)、晚期轻度认知障碍(LMCI)和正常受试者(NC)。所提出的方法对于NC vs SMC、SMC vs EMCI、SMC vs LMCI和EMCI vs LMCI等4种不同类型数据,诊断结果分别为76.67%、79.07%、80.56%和74.29%,与其他传统算法相比,分类准确率都有明显的提高,有望应用于对早期轻度认知障碍的诊断分析。     

基于随机森林算法的阿尔茨海默症医学影像分类

为实现阿尔茨海默症(AD)的医学影像分类,辅助医生对患者的病情进行准确判断,本研究对采集的34名AD患者、35名轻度认知障碍患者和35名正常对照组成员的功能磁共振影像进行特征提取和分类,具体思路包括:首先利用皮尔逊相关系数计算脑区之间的功能连接,然后采用随机森林算法对被试不同脑区之间的功能连接进行重要性度量及特征选择,最后使用支持向量机分类器进行分类,利用十倍交叉验证估算分类准确率。实验结果显示,随机森林算法可以对功能连接特征进行有效分析,同时得到AD发病过程的异常脑区,基于随机森林和SVM建立的分类模型对AD、轻度认知障碍的识别具有较好的效果,分类准确率可达90.68%,相关结论可以为AD的早期临床诊断提供客观参照。 【关键词】阿尔茨海默症;功能磁共振成像;随机森林;特征选择     

Increased fMRI responses during encoding in mild cognitive impairment

Structural and functional magnetic resonance imaging (fMRI) was performed on 21 healthy elderly controls, 14 subjects with mild cognitive impairment (MCI) and 15 patients with mild Alzheimer's disease (AD) to investigate changes in fMRI activation in relation to underlying structural atrophy. The fMRI paradigm consisted of associative encoding of novel picture-word pairs. Structural analysis of the brain was performed using voxel-based morphometry (VBM) and hippocampal volumetry. Compared to controls, the MCI subjects exhibited increased fMRI responses in the posterior hippocampal, parahippocampal and fusiform regions, while VBM revealed more atrophy in MCI in the anterior parts of the left hippocampus. Furthermore, the hippocampal volume and parahippocampal activation were negatively correlated in MCI, but not in controls or in AD. We suggest that the increased fMRI activation in MCI in the posterior medial temporal and closely connected fusiform regions is compensatory due to the incipient atrophy in the anterior medial temporal lobe.     

Hippocampus and entorhinal cortex in mild cognitive impairment and early AD

Magnetic resonance imaging (MRI) has been suggested as a useful tool in early diagnosis of Alzheimer's disease (AD). Based on MRI-derived volumes, we studied the hippocampus and entorhinal cortex (ERC) in 59 controls, 65 individuals with mild cognitive impairment (MCI) and 48 patients with AD. The controls and individuals with MCI were derived from population-based cohorts. Volumes of the hippocampus and ERC were significantly reduced in the following order: control > MCI > AD. Stepwise discriminant function analysis showed that the most efficient overall classification between controls and individuals with MCI subjects was achieved with ERC measurements (65.9%). However, the best overall classification between controls and AD patients (90.7%), and between individuals with MCI and AD patients (82.3%) was achieved with hippocampal volumes. Our results suggest that the ERC atrophy precedes hippocampal atrophy in AD. The ERC volume loss is dominant over the hippocampal volume loss in MCI, whereas more pronounced hippocampal volume loss appears in mild AD.     

A supervised method to assist the diagnosis and monitor progression of Alzheimer's disease using data from an fMRI experiment

Objective

The aim of this work is to provide a supervised method to assist the diagnosis and monitor the progression of the Alzheimer's disease (AD) using information which can be extracted from a functional magnetic resonance imaging (fMRI) experiment.

Methods and materials

The proposed method consists of five stages: (a) preprocessing of fMRI data, (b) modeling of the fMRI voxel time series using a generalized linear model, (c) feature extraction from the fMRI experiment, (d) feature selection, and (e) classification using the random forests algorithm. In the last stage we employ features that were extracted from the fMRI and other features such as demographics, behavioral and volumetric measures. The aim of the classification is twofold: first to diagnose AD and second to classify AD as very mild and mild.

Results

The method is evaluated using data from 41 subjects. The stage of AD is established using the Washington University Alzheimer's Disease Research Center recruitment and assessment procedures. The method classifies a patient as healthy or demented with 84% sensitivity and 92.3% specificity, and the stages of AD with 81% and 87% accuracy for the three class and the four class problem, respectively.

Conclusions

The method is advantageous since it is fully automated and for the first time the diagnosis and staging of the disease are addressed using fMRI.     

Memory profiling with paired associate learning in Alzheimer's disease, mild cognitive impairment, and healthy aging

Mild cognitive impairment (MCI) is associated with increased risk of developing Alzheimer's disease (AD), but up to 40% of cases do not develop AD. Examining a case's specific memory profile may help distinguish which MCI cases will progress to AD: An encoding profile is suggestive of incipient AD, whereas a retrieval profile suggests an alternative etiology. Paired associate learning (PAL) tasks are sensitive for preclinical and early detection of AD, but existing tasks do not enable memory profiling. We developed a novel PAL task enabling the differentiation of memory profiles in 19 people with AD, 17 people with amnestic MCI, and 33 normal elderly controls. Unexpectedly, the AD group demonstrated a retrieval profile for PAL using yes-no recognition, although an encoding profile was evident for forced-choice recognition and for the California Verbal Learning Test--Second Edition (Delis, Kramer, Kaplan, & Ober, 2000). There was considerable heterogeneity within the AD and MCI groups as well as intraindividual discordance for memory profiles. The findings challenge the clinical application of memory profiling in the differential diagnosis of AD, and, by extension, question its potential application in the assessment of MCI.     

Activation of brain regions vulnerable to Alzheimer's disease: the effect of mild cognitive impairment

This study examined the functionality of the medial temporal lobe (MTL) and posterior cingulate (PC) in mild cognitive impairment amnestic type (MCI), a syndrome that puts patients at greater risk for developing Alzheimer disease (AD). Functional MRI (fMRI) was used to identify regions normally active during encoding of novel items and recognition of previously learned items in a reference group of 77 healthy young and middle-aged adults. The pattern of activation in this group guided further comparisons between 14 MCI subjects and 14 age-matched controls. The MCI patients exhibited less activity in the PC during recognition of previously learned items, and in the right hippocampus during encoding of novel items, despite comparable task performance to the controls. Reduced fMRI signal change in the MTL supports prior studies implicating the hippocampus for encoding new information. Reduced signal change in the PC converges with recent research on its role in recognition in normal adults as well as metabolic decline in people with genetic or cognitive risk for AD. Our results suggest that a change in function in the PC may account, in part, for memory recollection failure in AD.     

结合表型信息的阿尔兹海默症图卷积神经网络分类方法研究

阿尔兹海默症(AD)的早期检测与发现具有重要的临床和社会意义.由于AD患者的功能性脑网络拓扑性质存在异常变化,并且不同表型类型人群中阿尔兹海默症的患病率也存在着较大差异,因此将脑网络特征和表型信息结合构建训练特征,用于阿尔兹海默症不同阶段的分类.同时,图卷积神经网络(GCN)分类方法被证明是目前对图数据学习任务的最佳选...     

Dimensionality reduced cortical features and their use in the classification of Alzheimer's disease and mild cognitive impairment

Features defined on the cortical surface derived from magnetic resonance imaging provide important information to distinguish normal controls from Alzheimer's disease (AD) and mild cognitive impairment (MCI). We adopted cortical thickness and sulcal depth, parameterized by three dimensional meshes, as our feature. The cortical feature is high dimensional and direct use of it is problematic in a modern classifier due to small sample size problem. We applied manifold learning to reduce the dimensionality of the feature and then tested the usage of the dimensionality reduced feature with a support vector machine classifier. A leave-one-out cross-validation was adopted for quantifying classifier performance. We chose principal component analysis (PCA) as the manifold learning method. We applied PCA to a region of interest within the cortical surface. Our classification performance was at least on par for the AD/normal and MCI/normal groups and significantly better for the AD/MCI groups compared to recent studies. Our approach was tested using 25 AD, 25 MCI, and 50 normal control patients from the OASIS database.     

基于磁共振图像的帕金森病计算机辅助诊断研究进展

近年来,由于帕金森病(PD)的临床复杂性与多模态磁共振(MR)图像的高维性,如何有效挖掘图像中特异性标记PD的影像生物标志物、建立高效的PD计算机辅助诊断(CAD)模型是研究中极具挑战性的问题。综述目前国内外研究进展,进一步分析MR多模态特征提取、特征选择、分类器模型等传统机器学习方法建立CAD模型的关键技术,并简要概述基于深度学习方法在早期PD分类诊断中的应用。指出基于多模态MR图像,采用机器学习或深度学习方法构建CAD模型,能够客观、准确地识别PD患者,对提高早期PD诊断的准确性具有很大价值和应用前景。今后研究应更深入挖掘多模态MR图像中的潜在标记PD的影像生物指标,开发更高阶的CAD模型,以辅助早期PD的临床智能诊断。